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Past and future evolution of French Alpine glaciers in a changing climate: a deep learning glacio-hydrological modelling approach / Jordi Bolibar Navarro (2020)
Titre : Past and future evolution of French Alpine glaciers in a changing climate: a deep learning glacio-hydrological modelling approach Type de document : Thèse/HDR Auteurs : Jordi Bolibar Navarro, Auteur ; Antoine Rabatel, Auteur ; Isabelle Gouttevin, Auteur ; Eric Sauquet, Auteur Editeur : Grenoble [France] : Université Grenoble Alpes Année de publication : 2020 Importance : 143 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse pour obtenir le grade de Docteur de l'Université Grenoble Alpes, Spécialité : Sciences de la Terre et de l’Univers et de l’EnvironnementLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] Alpes (France)
[Termes IGN] analyse diachronique
[Termes IGN] apprentissage automatique
[Termes IGN] bilan de masse
[Termes IGN] changement climatique
[Termes IGN] glacier
[Termes IGN] modèle de simulation
[Termes IGN] modèle hydrographique
[Termes IGN] modèle numérique de surface
[Termes IGN] réseau neuronal artificielIndex. décimale : THESE Thèses et HDR Résumé : (auteur) The European Alps are among the most affected regions in the world by climate change, displaying some of the strongest glacier retreat rates. Long-term interactions between society, mountain ecosystems and glaciers in the region raise important questions on the future evolution of glaciers and their derived environmental and socioeconomical impacts. In order to correctly assess the regional response of glaciers in the French Alps to climate change, there is a need for adequate modelling tools. In this work, we explore new ways to tackle both glacier evolution and glacio-hydrological modelling at a regional scale. Glacier evolution modelling has traditionally been performed using empirical or physical approaches, which are becoming increasingly challenging to optimize with the ever growing amount of available data. Here, we present, to our knowledge, the first effort ever to apply deep learning (i.e. deep artificial neural networks) to simulate the evolution of glaciers. Since both the climate and glacier systems are highly nonlinear, traditional linear mass balance models offer a limited representation of climate-glacier interactions. We show how important nonlinearities in glacier mass balance are captured by deep learning, substantially improving model performance over linear methods.This novel method was first applied in a study to reconstruct annual mass balance changes for all glaciers in the French Alps for the 1967-2015 period. Using climate reanalyses, topographical data and glacier inventories, we demonstrate how such an approach can be successfully used to reconstruct large-scale mass balance changes from observations. This study also offered new insights on how glaciers evolved in the French Alps during the last half century, confirming the rather neutral observed mass balance rates in the 1980s and displaying a well-marked acceleration in mass loss from the 2000s onwards. Important differences between regions are found, with the Mont-Blanc massif presenting the lowest mass loss and the Chablais being the most affected one. Secondly, we applied this modelling framework to simulate the future evolution of all glaciers in the region under multiple (N=29) climate change scenarios. Our estimates indicate that most ice volume in the region will be lost by the end of the 21st century independently from future climate scenarios. We predict average glacier volume losses of 74%, 80% and 88% under RCP 2.6 (n=3), RCP 4.5 (n=13) and RCP 8.5 (n=13), respectively. By the end of the 21st century the French Alps will be largely ice-free, with glaciers only remaining in the Mont-Blanc and Pelvoux massifs. Note de contenu : Introduction
1- Glaciers
2- Glacierized mountain catchments
3- OutlookNuméro de notice : 28311 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Sciences de la Terre et de l’Univers et de l’Environnement : Grenoble : 2020 Organisme de stage : Institut des Géosciences de l’Environnement (Grenoble) DOI : sans En ligne : https://tel.archives-ouvertes.fr/tel-03052063v2/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98202
Titre : Processing and analysis of hyperspectral data Type de document : Monographie Auteurs : Jie Chen, Éditeur scientifique ; Yingying Song, Éditeur scientifique ; Hengchao Li, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2020 Importance : 140 p. ISBN/ISSN/EAN : 978-1-78985-109-0 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage profond
[Termes IGN] classification dirigée
[Termes IGN] classification non dirigée
[Termes IGN] image à haute résolution
[Termes IGN] image hyperspectrale
[Termes IGN] image proche infrarouge
[Termes IGN] qualité des eaux
[Termes IGN] signature spectrale
[Termes IGN] turbidité des eauxRésumé : (Editeur) Hyperspectral imagery has received considerable attention in the last decade as it provides rich spectral information and allows the analysis of objects that are unidentifiable by traditional imaging techniques. It has a wide range of applications, including remote sensing, industry sorting, food analysis, biomedical imaging, etc. However, in contrast to RGB images from which information can be intuitively extracted, hyperspectral data is only useful with proper processing and analysis. This book covers theoretical advances of hyperspectral image processing and applications of hyperspectral processing, including unmixing, classification, super-resolution, and quality estimation with classical and deep learning methods. Note de contenu : Section One - Theoretical advances of hyperspectral image processing
Chapter 1 - Hyperspectral endmember extraction techniques
Chapter 2 - Hyperspectral image classification
Chapter 3 - Hyperspectral image super-resolution using optimization and DCNN-based methods
Chapter 4 - Fast chaotic encryption for hyperspectral images
Section Two - Applications of hyperspectral image processing
Chapter 5 - NIR hyperspectral imaging for mapping of moisture content distribution in tea buds during dehydration
Chapter 6 - Use of hyperspectral remote sensing to estimate water qualityNuméro de notice : 26560 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.78179 En ligne : http://doi.org/10.5772/intechopen.78179 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98243
Titre : Recent advances in geographic information system for Earth sciences Type de document : Monographie Auteurs : Yosoon Choi, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2020 Importance : 264 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-03936-490-9 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] analyse spatiale
[Termes IGN] apprentissage profond
[Termes IGN] bassin hydrographique
[Termes IGN] cartographie thématique
[Termes IGN] codage
[Termes IGN] cognition
[Termes IGN] développement durable
[Termes IGN] effondrement de terrain
[Termes IGN] interface homme-machine
[Termes IGN] modèle dynamique
[Termes IGN] modèle numérique de surface
[Termes IGN] oculométrie
[Termes IGN] outil d'aide à la décision
[Termes IGN] planification urbaine
[Termes IGN] QGIS
[Termes IGN] réseau de transport
[Termes IGN] réseau social
[Termes IGN] utilisation du solRésumé : (éditeur) Geographic information systems (GISs) have played a vital role in Earth sciences by providing a powerful means of observing the world and various tools for solving complex problems. The scientific community has used GISs to reveal fascinating details about the Earth and other planets. This book on recent advances in GIS for Earth sciences includes 12 publications from esteemed research groups worldwide. The research and review papers in this book belong to the following broad categories: Earth science informatics (geoinformatics), mining, hydrology, natural hazards, and society. Note de contenu : 1- Recent advances in geographic information system for earth sciences
2- An efficient parallel algorithm for polygons overlay analysis
3- Vector map random encryption algorithm based on multi-scale simplification and Gaussian distribution
4- Evaluation of effective cognition for the QGIS processing modeler
5- Geo-sensor framework and composition toolbox for efficient deployment of multiple spatial context service platforms in sensor networks
6- Review of GIS-based applications for mining: Planning, operation, and environmental management
7- A tightly coupled GIS and spatiotemporal modeling for methane emission simulation in the underground coal mine system
8- Evaluation of reliable digital elevation model resolution for TOPMODEL in two mountainous watersheds, South Korea
9- Spatiotemporal changes of urban rainstorm-related micro-blogging activities in response to rainstorms: A case study in Beijing, China
10- Rainfall induced landslide studies in Indian Himalayan region: A critical review
11- GIS-based evaluation of landslide susceptibility models using certainty factors and functional trees-based ensemble techniques
12- Spatiotemporal dynamics and obstacles of the multi-functionality of land use in Xiangxi, China
13- Analyzing spatial community pattern of network traffic flow and its variations across time based on taxi GPS trajectoriesNuméro de notice : 28439 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-03936-490-9 En ligne : https://doi.org/10.3390/books978-3-03936-490-9 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98876
Titre : Recent advances in image restoration with applications to real world problems Type de document : Monographie Auteurs : Chiman Kwan, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2020 ISBN/ISSN/EAN : 978-1-83968-356-5 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] apprentissage non-dirigé
[Termes IGN] données spatiotemporelles
[Termes IGN] extraction de modèle
[Termes IGN] fusion d'images
[Termes IGN] image hyperspectrale
[Termes IGN] modèle numérique de terrain
[Termes IGN] reconstruction 3D
[Termes IGN] restauration d'imageRésumé : (Editeur) In the past few decades, imaging hardware has improved tremendously in terms of resolution, making widespread usage of images in many diverse applications on Earth and planetary missions. However, practical issues associated with image acquisition are still affecting image quality. Some of these issues such as blurring, measurement noise, mosaicing artifacts, low spatial or spectral resolution, etc. can seriously affect the accuracy of the aforementioned applications. This book intends to provide the reader with a glimpse of the latest developments and recent advances in image restoration, which includes image super-resolution, image fusion to enhance spatial, spectral resolution, and temporal resolutions, and the generation of synthetic images using deep learning techniques. Some practical applications are also included. Note de contenu :
1. Introductory Chapter: Recent Advances in Image Restoration
2. Resolution Enhancement of Hyperspectral Data Exploiting Real Multi-Platform Data
3. Application of Deep Learning Approaches for Enhancing Mastcam Images
4. Generative Adversarial Networks for Visible to Infrared Video Conversion
5. Style-Based Unsupervised Learning for Real-World Face Image Super-Resolution
6. Spatiotemporal Fusion in Remote Sensing
7. 3D Reconstruction through Fusion of Cross-View Images
8. Practical Digital Terrain Model Extraction Using Image Inpainting TechniquesNuméro de notice : 26695 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.90607 Date de publication en ligne : 04/11/2020 En ligne : https://doi.org/10.5772/intechopen.90607 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99081
Titre : Recent trends in artificial neural networks Type de document : Monographie Auteurs : Ali Sadollah, Éditeur scientifique ; Carlos M. Travieso-Gonzalez, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2020 Importance : 150 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-1-78985-859-4 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] algorithme génétique
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage profond
[Termes IGN] classification floue
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'objet
[Termes IGN] logique floue
[Termes IGN] réseau neuronal artificielRésumé : (éditeur) Artificial intelligence (AI) is everywhere and it's here to stay. Most aspects of our lives are now touched by artificial intelligence in one way or another, from deciding what books or flights to buy online to whether our job applications are successful, whether we receive a bank loan, and even what treatment we receive for cancer. Artificial Neural Networks (ANNs) as a part of AI maintains the capacity to solve problems such as regression and classification with high levels of accuracy. This book aims to discuss the usage of ANNs for optimal solving of time series applications and clustering. Bounding of optimization methods particularly metaheuristics considered as global optimizers with ANNs make a strong and reliable prediction tool for handling real-life application. This book also demonstrates how different fields of studies utilize ANNs proving its wide reach and relevance. Note de contenu : 1- Time series from clustering: An approach to forecast crime patterns
2- Encountered problems of time series with neural networks: Models and architectures
3- Metaheuristics and artificial neural networks
4- An improved algorithm for optimising the production of biochemical systems
5- Object recognition using convolutional neural networks
6- Prediction of wave energy potential in India: A fuzzy-ANN approach
7- Deep learning training and benchmarks for Earth observation images: Data sets, features, and procedures
8- Data mining technology for structural control systems: Concept, development, and comparisonNuméro de notice : 28497 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.77409 En ligne : https://doi.org/10.5772/intechopen.77409 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99247 PermalinkPermalinkPermalinkPermalinkSatellite image time series classification with pixel-set encoders and temporal self-attention / Vivien Sainte Fare Garnot (2020)PermalinkPermalinkSpatio-Temporal Prediction of the Epidemic Spread of Dangerous Pathogens Using Machine Learning Methods / Wolfgang B. Hamer in ISPRS International journal of geo-information, Vol 9 n° 1 (January 2020)PermalinkPermalinkSuperpixel-enhanced deep neural forest for remote sensing image semantic segmentation / Li Mi in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)PermalinkSystème de traitement d’images temps réel dédié à la mesure de champs denses de déplacements et de déformations / Seyfeddine Boukhtache (2020)PermalinkTorch-Points3D: A modular multi-task framework for reproducible deep learning on 3D point clouds / Thomas Chaton (2020)PermalinkUnderwater field equipment of a network of landmarks optimized for automatic detection by AI / Laurent Beaudoin (2020)PermalinkUnsupervised satellite image time series analysis using deep learning techniques / Ekaterina Kalinicheva (2020)PermalinkLe vandalisme de l'information géographique volontaire : analyse exploratoire et proposition d'une méthodologie de détection automatique / Quy Thy Truong (2020)PermalinkVers une occupation du sol France entière par imagerie satellite à très haute résolution / Tristan Postadjian (2020)PermalinkVery high resolution land cover mapping of urban areas at global scale with convolutional neural network / Thomas Tilak (2020)PermalinkPermalinkPermalinkPermalinkShip identification and characterization in Sentinel-1 SAR images with multi-task deep learning / Clément Dechesne in Remote sensing, Vol 11 n° 24 (December-2 2019)Permalink